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Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow.

, , , и . IEEE Trans. Biomed. Eng., 63 (3): 636-646 (2016)

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Raúl Gutiérrez

Lirio Gutiérrez

Alicia Gutiérrez González

Rafael Gutiérrez Girardot

Juan Vargas-Gutiérrez

 

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Apnea-hypopnea index estimation from spectral analysis of airflow recordings., , , , , и . EMBC, стр. 3444-3447. IEEE, (2012)Automated analysis of nocturnal oximetry as screening tool for childhood obstructive sleep apnea-hypopnea syndrome., , , , , , , , и . EMBC, стр. 2800-2803. IEEE, (2015)Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment., , , , , , и . Entropy, 20 (1): 35 (2018)Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis., , , , , , , и . Comput. Biol. Medicine, (2021)A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea., , , , , , , и . EMBC, стр. 1-4. IEEE, (2023)Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost., , , , , , , , и . EMBC, стр. 167-170. IEEE, (2018)Characterization of Cardiopulmonary Coupling in Pediatric Patients with Obstructive Sleep Apnea., , , , , , , , , и 1 other автор(ы). CinC, стр. 1-4. IEEE, (2023)Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home., , , , , , , , и . Entropy, 19 (6): 284 (2017)An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals., , , , , , , , и . Biomed. Signal Process. Control., 87 (Part B): 105490 (января 2024)Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. Entropy 2021, 23, 1016., , , , , , , и . Entropy, 23 (11): 1375 (2021)